Orientation effects in copper phthalocyanine films studied by EPR

Organic semiconductors have already found commercial applications in for example displays with organic light-emitting diodes (OLEDs) and great advances are also being made in other areas, such as organic field-effect transistors and organic solar cells. [1] The organic semicondutor group of materials known as metal phthalocyanines (MPc’s) is interesting for applications such as large area solar cells due to their optoelectronic properties coupled with the possibility of easily and cheaply fabricating thin films of MPc’s. [1, 2]

Many of the properties of organic semiconductors, such as magnetism, light absorption and charge transport, show orientational anisotropy. [2, 3] To maximise the efficiency of a device based on these materials it is therefore important to study the molecular orientation in films and to assess the influence of different growth conditions and substrate treatments. X-ray diffraction is a well established and powerful technique for studying texture (and hence molecular orientation)_in crystalline materials, but cannot provide any information about amorphous or nanocrystalline films. In this paper we present a continuous wave X-band EPR study using the anisotropy of the CuPc EPR spectrum [4] to determine the orientation effects in different types of CuPc films. From these measurements we also gain insight into the molecular arrangement of films of CuPc mixed with the isomorphous H2Pc and with C60 in films typical of real solar cell systems.

Interstitial Nitrogen in Diamond – Optical and EPR Studies

Nitrogen is one of the most common impurities in diamond. On a substitutional site it acts as a deep donor, approximately 1.7 eV below the conduction band. Irradiation of nitrogen containing diamond and subsequent annealing creates the nitrogen vacancy centre, which has recently attracted much attention for quantum information processing application. Another possible product of irradiation and annealing of nitrogen containing diamond is interstitial nitrogen. Presumably, a mobile carbon interstitial migrates to a substitutional nitrogen to produce an interstitial nitrogen complex which may or may not be mobile. The configuration(s) of interstitial nitrogen related defects (e.g. bond centred, [001]-split) are not known. An infra-red (IR) absorption peak at 1450 cm-1 labelled H1a has been associated with an nitrogen interstitial complex. [1] Theoretical modelling suggested that this IR local mode is due to a bond centred nitrogen interstitial [2]. However, more recent modelling [3] suggests that this defect is mobile at temperatures were H1a is stable and instead assign H1a to two nitrogen atoms occupying a single lattice site in a [001]-split configuration. To date no electron paramagnetic resonance (EPR) spectra have been conclusively associated with an interstitial nitrogen defect.

In this study we present data from new EPR and optical absorption studies in combination with uniaxial stress of nitrogen interstitial related defects in electron irradiated and annealed nitrogen doped diamond. These measurements yield symmetry information about the defects allowing us to determine which of the proposed models are possible. EPR spectra of nitrogen interstitial related defects in samples isotopically enriched with 15N are reported and we show that these explain the lack of previous EPR data for these defects. Correlations between the IR absorbance and the integrated intensity of the new EPR defects are studied for varying irradiation doses and annealing temperatures.

Knowledge, innovation and emulation in the evolutionary thought of Bernard Mandeville

Of the early modern writers on the division of labour, Bernard Mandeville alone extended it to all aspects of human activity and emphasised its role in a cumulative process of evolution in which each generation modified and built on what had been achieved by earlier generations. This required exploration of the mechanisms through which new knowledge was developed as well as the means by which knowledge was transmitted between the generations. The present article examines Mandeville’s treatment of these mechanisms and explores their theoretical origins. It examines Mandeville’s understanding of the role of the division of labour in facilitating discovery and learning and the role of education and imitation in transmitting social knowledge. It shows that, for Mandeville, innovators were people of ordinary capacity who were alert to the opportunities and challenges of their environment. As a result of specialisation, they possessed tacit knowledge which was actualised in what they did rather than in theoretical propositions. Mandeville’s evolutionary thought influenced subsequent writers on political economy and evolutionary social thinkers. It may also have had some influence on Charles Darwin, though it is not, in itself, Darwinian. © The Author 2013. Published by Oxford University Press on behalf of the Cambridge Political Economy Society. All rights reserved.

Role of vascular endothelial growth factor and placental growth factors during retinal vascular development and hyaloid regression

PURPOSE. Vascular endothelial growth factor (VEGF)-A and placental growth factor (PIGF) are members of a large group of homologous peptides identified as the VEGF family. Although VEGF-A is known to act as a potent angiogenic peptide in the retina, the vasoactive function of PIGF in this tissue is less well defined. This study has sought to elucidate the expression patterns and modulatory role of these growth factors during retinal vascular development and hyaloid regression in the neonatal mouse. METHODS. C57BL6J mice were killed at postnatal days (P)1, P3, P5, P7, P9, and P11. The eyes were enucleated and processed for in situ hybridization and immunocytochemistry and the retinas extracted for total protein or RNA. Separate groups of neonatal mice were also injected intraperitoneally daily from P2 through P9 with either VEGF-neutralizing antibody, PIGF-neutralizing antibody, isotype immunoglobulin (Ig)-G, or phosphate-buffered saline (PBS). The mice were then perfused with fluorescein isothiocyanate (FITC)-dextran, and the eyes were subsequently embedded in paraffin wax or flat mounted. RESULTS. Quantitative (real-time) reverse transcription-polymerase chain reaction (RT-PCR) demonstrated similar expression patterns of VEGF-A and PIGF mRNA during neonatal retinal development, although the fluctuation between time periods was greater overall for VEGF-A. The localization of VEGF-A and PIGF in the retina, as revealed by in situ hybridization and immunohistochemistry, was also similar. Neutralization of VEGF-A caused a significant reduction in the hyaloid and retinal vasculature, whereas PIGF antibody treatment caused a marked persistence of the hyaloid without significantly affecting retinal vascular development. CONCLUSIONS. Although having similar expression patterns in the retina, these growth factors appear to have distinct modulatory influences during normal retinal vascular development and hyaloid regression.

Determinants of WTP for Prosecco wine: A latent class regression with attitudinal responses

Purpose – Under investigation is Prosecco wine, a sparkling white wine from North-East Italy.
Information collection on consumer perceptions is particularly relevant when developing market
strategies for wine, especially so when local production and certification of origin play an important
role in the wine market of a given district, as in the case at hand. Investigating and characterizing the
structure of preference heterogeneity become crucial steps in every successful marketing strategy. The
purpose of this paper is to investigate the sources of systematic differences in consumer preferences.
Design/methodology/approach – The paper explores the effect of inclusion of answers to
attitudinal questions in a latent class regression model of stated willingness to pay (WTP) for this
specialty wine. These additional variables were included in the membership equations to investigate
whether they could be of help in the identification of latent classes. The individual specific WTPs from
the sampled respondents were then derived from the best fitting model and examined for consistency.
Findings – The use of answers to attitudinal question in the latent class regression model is found to
improve model fit, thereby helping in the identification of latent classes. The best performing model
obtained makes use of both attitudinal scores and socio-economic covariates identifying five latent
classes. A reasonable pattern of differences in WTP for Prosecco between CDO and TGI types were
derived from this model.
Originality/value – The approach appears informative and promising: attitudes emerge as
important ancillary indicators of taste differences for specialty wines. This might be of interest per se
and of practical use in market segmentation. If future research shows that these variables can be of use
in other contexts, it is quite possible that more attitudinal questions will be routinely incorporated in
structural latent class hedonic models.

Analysis of IL6 Signal Transduction Model using Reduced Rank Regression

This work presents the application of reduced rank regression to the field of systems biology. A computational approach is used to investigate the mechanisms of the janus-associated kinases/signal transducers and transcription factors (JAK/STAT) and mitogen activated protein kinases (MAPK) signal transduction pathways in hepatic cells stimulated by interleukin-6. The results obtained identify the contribution of individual reactions to the dynamics of the model. These findings are compared to previously available results from sensitivity analysis of the model which focused on the parameters involved and their effect. This application of reduced rank regression allows for an understanding of the individual reaction terms involved in the modelled signal transduction pathways and has the benefit of being computationally inexpensive. The obtained results complement existing findings and also confirm the importance of several protein complexes in the MAPK pathway which hints at benefits that can be achieved by further refining the model.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

An application of bi-directional evolutionary structural optimisation for optimising energy absorbing structures using a material damage model

The Bi-directional Evolutionary Structural Optimisation (BESO) method is a numerical topology optimisation method developed for use in finite element analysis. This paper presents a particular application of the BESO method to optimise the energy absorbing capability of metallic structures. The optimisation objective is to evolve a structural geometry of minimum mass while ensuring that the kinetic energy of an impacting projectile is reduced to a level which prevents perforation. Individual elements in a finite element mesh are deleted when a prescribed damage criterion is exceeded. An energy absorbing structure subjected to projectile impact will fail once the level of damage results in a critical perforation size. It is therefore necessary to constrain an optimisation algorithm from producing such candidate solutions. An algorithm to detect perforation was implemented within a BESO framework which incorporated a ductile material damage model.

Finite domination and Novikov rings: Laurent polynomial rings in two variables

Let C be a bounded cochain complex of finitely generatedfree modules over the Laurent polynomial ring L = R[x, x−1, y, y−1].The complex C is called R-finitely dominated if it is homotopy equivalentover R to a bounded complex of finitely generated projective Rmodules.Our main result characterises R-finitely dominated complexesin terms of Novikov cohomology: C is R-finitely dominated if andonly if eight complexes derived from C are acyclic; these complexes areC ⊗L R[[x, y]][(xy)−1] and C ⊗L R[x, x−1][[y]][y−1], and their variants obtainedby swapping x and y, and replacing either indeterminate by its inverse.

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. © 2013 IEEE.

MSC-clustering and forward stepwise regression for virtual metrology in highly correlated input spaces

Increasingly semiconductor manufacturers are exploring opportunities for virtual metrology (VM) enabled process monitoring and control as a means of reducing non-value added metrology and achieving ever more demanding wafer fabrication tolerances. However, developing robust, reliable and interpretable VM models can be very challenging due to the highly correlated input space often associated with the underpinning data sets. A particularly pertinent example is etch rate prediction of plasma etch processes from multichannel optical emission spectroscopy data. This paper proposes a novel input-clustering based forward stepwise regression methodology for VM model building in such highly correlated input spaces. Max Separation Clustering (MSC) is employed as a pre-processing step to identify a reduced srt of well-conditioned, representative variables that can then be used as inputs to state-of-the-art model building techniques such as Forward Selection Regression (FSR), Ridge regression, LASSO and Forward Selection Ridge Regression (FCRR). The methodology is validated on a benchmark semiconductor plasma etch dataset and the results obtained are compared with those achieved when the state-of-art approaches are applied directly to the data without the MSC pre-processing step. Significant performance improvements are observed when MSC is combined with FSR (13%) and FSRR (8.5%), but not with Ridge Regression (-1%) or LASSO (-32%). The optimal VM results are obtained using the MSC-FSR and MSC-FSRR generated models. © 2012 IEEE.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.